Leveraging functional annotations in genetic risk prediction for human complex diseases

TLDR

Genetic risk prediction is a key goal in precision medicine, yet its accuracy remains moderate because many disease‑associated variants are functionally uncharacterized and effect sizes are hard to estimate in the presence of linkage disequilibrium. This study introduces AnnoPred, a framework that integrates diverse genomic and epigenomic functional annotations to improve genetic risk prediction for complex diseases. AnnoPred is trained on GWAS summary statistics within a Bayesian framework that models functional annotations and incorporates linkage disequilibrium from reference genotype data. AnnoPred consistently outperforms existing risk‑prediction methods in simulations and real datasets.

Abstract

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.